Amazon SageMaker Feature Store is a fully managed capability that makes it easy to compute, store, and retrieve features for training and deploying AI models. SageMaker Feature Store now supports new capabilities for high-throughput feature ingestion, record discovery, and offline store cataloging. Data scientists can now write multiple records across multiple feature groups in a single request with BatchWriteRecord, list the records stored in a feature group without knowing each record identifier in advance with ListRecords, and create tables and databases with custom names in the offline store.

Data scientists can use BatchWriteRecord to ingest feature data at scale with fewer API calls and lower latency than writing one record at a time. BatchWriteRecord targets the online store, the offline store, or both, returns individual record failures without failing the entire request, and supports time-to-live settings at the record, request, and feature group level. With ListRecords, data scientists can retrieve the record identifiers in a feature group, one page at a time, to browse and audit feature group contents, recover record identifiers, and manage the record lifecycle. When configuring an offline store, data scientists can also create Glue and Iceberg tables with custom names. These capabilities enable data scientists to ingest features at scale and manage the records stored in SageMaker Feature Store without building custom tooling.

These capabilities are available in all AWS Regions where Amazon SageMaker Feature Store is available. For more information, see Amazon Feature Store Runtime and Offline Store Configuration documentation.

 

​Amazon SageMaker Feature Store is a fully managed capability that makes it easy to compute, store, and retrieve features for training and deploying AI models. SageMaker Feature Store now supports new capabilities for high-throughput feature ingestion, record discovery, and offline store cataloging. Data scientists can now write multiple records across multiple feature groups in a single request with BatchWriteRecord, list the records stored in a feature group without knowing each record identifier in advance with ListRecords, and create tables and databases with custom names in the offline store. Data scientists can use BatchWriteRecord to ingest feature data at scale with fewer API calls and lower latency than writing one record at a time. BatchWriteRecord targets the online store, the offline store, or both, returns individual record failures without failing the entire request, and supports time-to-live settings at the record, request, and feature group level. With ListRecords, data scientists can retrieve the record identifiers in a feature group, one page at a time, to browse and audit feature group contents, recover record identifiers, and manage the record lifecycle. When configuring an offline store, data scientists can also create Glue and Iceberg tables with custom names. These capabilities enable data scientists to ingest features at scale and manage the records stored in SageMaker Feature Store without building custom tooling.
These capabilities are available in all AWS Regions where Amazon SageMaker Feature Store is available. For more information, see Amazon Feature Store Runtime and Offline Store Configuration documentation.  

Amazon SageMaker Unified Studio now supports custom asset types for IAM-based domains. With custom asset types, domain administrators can catalog any format of asset within the SageMaker Unified Studio, such as medical imaging files in Amazon S3, revenue dashboards built in PowerBI, or PDF research reports generated by a third-party platform. Custom asset types bring all assets, regardless of their underlying format, into the SageMaker catalog so teams can search, discover, and subscribe to them without needing separate tools or processes.

To get started, an administrator can create a custom asset type with a name, description, and optional metadata forms that define the fields each asset should carry. Individual assets can then be created from that type, enriched with glossary terms and README documentation to add business context for humans and AI agents, and published for discovery. Once published, anyone in the domain can find the asset by name, type, or glossary term and request a subscription through the same governed workflow used for all other catalog assets.

Custom asset types for IAM-based domains are available in all AWS Regions where Amazon SageMaker Unified Studio is available. To learn more, visit the SageMaker Unified Studio user guide. 

 

​Amazon SageMaker Unified Studio now supports custom asset types for IAM-based domains. With custom asset types, domain administrators can catalog any format of asset within the SageMaker Unified Studio, such as medical imaging files in Amazon S3, revenue dashboards built in PowerBI, or PDF research reports generated by a third-party platform. Custom asset types bring all assets, regardless of their underlying format, into the SageMaker catalog so teams can search, discover, and subscribe to them without needing separate tools or processes.
To get started, an administrator can create a custom asset type with a name, description, and optional metadata forms that define the fields each asset should carry. Individual assets can then be created from that type, enriched with glossary terms and README documentation to add business context for humans and AI agents, and published for discovery. Once published, anyone in the domain can find the asset by name, type, or glossary term and request a subscription through the same governed workflow used for all other catalog assets.
Custom asset types for IAM-based domains are available in all AWS Regions where Amazon SageMaker Unified Studio is available. To learn more, visit the SageMaker Unified Studio user guide.   

Amazon SageMaker Unified Studio Workflows now supports 19 new operators for Amazon Bedrock, Amazon S3 Tables, Amazon S3 Vectors, AWS Glue Data Catalog, and Amazon MWAA Serverless. With these operators, customers can add new tasks using the visual workflow creator to orchestrate these services without writing custom integration code.
With this launch, data workers and builders can create workflows that manage Bedrock guardrails, provision and delete S3 Tables and S3 Vectors resources, manage Glue Data Catalog tables and databases, and trigger MWAA Serverless workflow runs. This expands the breadth of AWS services you can orchestrate from SageMaker Unified Studio Workflows, reducing the need to switch between consoles or write custom DAG code.
This feature is available in all AWS Regions where Amazon SageMaker Unified Studio is available. For more information, see the AWS Region table.
To learn more, see Supported operators for Amazon MWAA Serverless workflows. To get started, see Serverless visual workflows in the Amazon SageMaker Unified Studio User Guide.

 

​Amazon SageMaker Unified Studio Workflows now supports 19 new operators for Amazon Bedrock, Amazon S3 Tables, Amazon S3 Vectors, AWS Glue Data Catalog, and Amazon MWAA Serverless. With these operators, customers can add new tasks using the visual workflow creator to orchestrate these services without writing custom integration code. With this launch, data workers and builders can create workflows that manage Bedrock guardrails, provision and delete S3 Tables and S3 Vectors resources, manage Glue Data Catalog tables and databases, and trigger MWAA Serverless workflow runs. This expands the breadth of AWS services you can orchestrate from SageMaker Unified Studio Workflows, reducing the need to switch between consoles or write custom DAG code. This feature is available in all AWS Regions where Amazon SageMaker Unified Studio is available. For more information, see the AWS Region table. To learn more, see Supported operators for Amazon MWAA Serverless workflows. To get started, see Serverless visual workflows in the Amazon SageMaker Unified Studio User Guide.  

AWS Client VPN is now available in four new regions: Canada West (Calgary), Mexico (Central) and two in Asia Pacific – New Zealand and Taipei. This fully managed service enables customers to securely connect their remote workforce to resources in AWS or on-premises networks.

AWS Client VPN eliminates the need for hardware VPN appliances and complex operational management through its pay-as-you-go model. Organizations can easily manage and monitor VPN connections through a single console.
To learn more about Client VPN:

• Visit the AWS Client VPN product page.
• Read the AWS Client VPN documentation.
• AWS Client VPN pricing page.

 

​AWS Client VPN is now available in four new regions: Canada West (Calgary), Mexico (Central) and two in Asia Pacific – New Zealand and Taipei. This fully managed service enables customers to securely connect their remote workforce to resources in AWS or on-premises networks.
AWS Client VPN eliminates the need for hardware VPN appliances and complex operational management through its pay-as-you-go model. Organizations can easily manage and monitor VPN connections through a single console. To learn more about Client VPN:

Visit the AWS Client VPN product page.
Read the AWS Client VPN documentation.
AWS Client VPN pricing page.  

AWS Builder Center now lets builders request free, time-limited sandbox environments directly from eligible workshops, eliminating the need for a personal AWS account, credit card, or concerns about unexpected charges. Previously, workshops could only be completed using a builder’s own AWS account. With sandbox environments, builders of all skill levels can safely deploy resources, write code, and experiment in a pre-provisioned AWS account.

Free sandbox environments are ideal for builders who want to gain practical AWS experience without setting up an account. Each sandbox provides 8 hours of access from activation, with automatic cleanup afterward. Builders can request one sandbox per week (resetting every Sunday), and most environments are ready within 15 minutes. Sandboxes are available for select workshops at launch, with more being enabled over time.

To get started, visit Workshops on AWS Builder Center.

 

​AWS Builder Center now lets builders request free, time-limited sandbox environments directly from eligible workshops, eliminating the need for a personal AWS account, credit card, or concerns about unexpected charges. Previously, workshops could only be completed using a builder’s own AWS account. With sandbox environments, builders of all skill levels can safely deploy resources, write code, and experiment in a pre-provisioned AWS account.
Free sandbox environments are ideal for builders who want to gain practical AWS experience without setting up an account. Each sandbox provides 8 hours of access from activation, with automatic cleanup afterward. Builders can request one sandbox per week (resetting every Sunday), and most environments are ready within 15 minutes. Sandboxes are available for select workshops at launch, with more being enabled over time.
To get started, visit Workshops on AWS Builder Center.  

Amazon Aurora DSQL change data capture (CDC) is now generally available, enabling you to stream real-time database changes to Amazon Kinesis Data Streams for event-driven architectures and data integration workflows.

Aurora DSQL CDC automatically captures the results of insert, update, and delete operations as change events and delivers them to Kinesis Data Streams with no infrastructure to manage. You can use CDC to synchronize data across microservices, trigger AWS Lambda functions, or deliver changes to Amazon S3, Amazon Redshift, and Amazon OpenSearch Service via Amazon Data Firehose. CDC streaming is designed to have zero impact on your database workload performance.

CDC streaming is available in all AWS Regions where Aurora DSQL is available. Get started with Aurora DSQL for free with the AWS Free Tier. To learn more about CDC in Aurora DSQL, visit the documentation page.

 

​Amazon Aurora DSQL change data capture (CDC) is now generally available, enabling you to stream real-time database changes to Amazon Kinesis Data Streams for event-driven architectures and data integration workflows. Aurora DSQL CDC automatically captures the results of insert, update, and delete operations as change events and delivers them to Kinesis Data Streams with no infrastructure to manage. You can use CDC to synchronize data across microservices, trigger AWS Lambda functions, or deliver changes to Amazon S3, Amazon Redshift, and Amazon OpenSearch Service via Amazon Data Firehose. CDC streaming is designed to have zero impact on your database workload performance. CDC streaming is available in all AWS Regions where Aurora DSQL is available. Get started with Aurora DSQL for free with the AWS Free Tier. To learn more about CDC in Aurora DSQL, visit the documentation page.  

Today, AWS Security Hub introduces Network Scanning, a capability that identifies resources in your environment that are reachable from the public internet.  Network Scanning probes your resources from the internet to detect actual reachability, not just what could be reachable based on security group rules and route tables. It discovers public IP addresses, virtual machines, and load balancers across your AWS and Azure environments, identifies reachable ports, and determines what services are running behind them. This complements Security Hub’s existing network reachability findings, which identify configurations that could make a resource reachable from the internet.  Network Scanning confirms actual reachability from the internet. Each reachable port generates a Security Hub finding with evidence of the port and service discovered. Security Hub Exposures then automatically correlates these findings with other findings and resource configurations to determine broader risk.

 

​Today, AWS Security Hub introduces Network Scanning, a capability that identifies resources in your environment that are reachable from the public internet.  Network Scanning probes your resources from the internet to detect actual reachability, not just what could be reachable based on security group rules and route tables. It discovers public IP addresses, virtual machines, and load balancers across your AWS and Azure environments, identifies reachable ports, and determines what services are running behind them. This complements Security Hub’s existing network reachability findings, which identify configurations that could make a resource reachable from the internet.  Network Scanning confirms actual reachability from the internet. Each reachable port generates a Security Hub finding with evidence of the port and service discovered. Security Hub Exposures then automatically correlates these findings with other findings and resource configurations to determine broader risk.  

Amazon Redshift now supports Graviton-based RG instances on the trailing track. Starting July 7, 2026, rg.4xlarge and rg.xlarge instance types are available for customers running workloads on the trailing track (P201).

The trailing track is designed for customers who prioritize stability for production workloads, running on a version already validated through the leading track. With RG instances now available on both tracks, customers can take advantage of AWS Graviton-powered performance – delivering up to 2.4x faster query performance than RA3 instances at 30% lower price per vCPU.

To get started, customers can provision a new cluster or resize an existing cluster to an rg.4xlarge or rg.xlarge instance type on the trailing track (P201) in the AWS Management Console, AWS CLI, or AWS SDKs.

For more information, see Amazon Redshift cluster versions.

 

​Amazon Redshift now supports Graviton-based RG instances on the trailing track. Starting July 7, 2026, rg.4xlarge and rg.xlarge instance types are available for customers running workloads on the trailing track (P201).
The trailing track is designed for customers who prioritize stability for production workloads, running on a version already validated through the leading track. With RG instances now available on both tracks, customers can take advantage of AWS Graviton-powered performance – delivering up to 2.4x faster query performance than RA3 instances at 30% lower price per vCPU. To get started, customers can provision a new cluster or resize an existing cluster to an rg.4xlarge or rg.xlarge instance type on the trailing track (P201) in the AWS Management Console, AWS CLI, or AWS SDKs. For more information, see Amazon Redshift cluster versions.  

Amazon Connect Customer now supports forecasting, capacity planning, and scheduling for Tasks and Emails, enabling you to plan and optimize your workforce across all workloads (Voice, Chat, Tasks, Emails). Connect Customer accounts for the unique characteristics of each channel, including concurrent work handling, duration of work from minutes to months, and distinct service level requirements, so your forecasts and schedules reflect how your operation actually runs. For example, if your agents handle email inquiries or tasks such as case processing alongside voice calls, you can now generate a unified forecast that accounts for all of these workloads and create schedules that efficiently allocate agents across channels. Thus enabling end-to-end workforce optimization within a single solution and ensuring consistent service levels across all customer interaction channels.

This feature is available in all AWS Regions where Amazon Connect Customer agent scheduling is available. To learn more about Amazon Connect Customer agent scheduling, click here.

 

​Amazon Connect Customer now supports forecasting, capacity planning, and scheduling for Tasks and Emails, enabling you to plan and optimize your workforce across all workloads (Voice, Chat, Tasks, Emails). Connect Customer accounts for the unique characteristics of each channel, including concurrent work handling, duration of work from minutes to months, and distinct service level requirements, so your forecasts and schedules reflect how your operation actually runs. For example, if your agents handle email inquiries or tasks such as case processing alongside voice calls, you can now generate a unified forecast that accounts for all of these workloads and create schedules that efficiently allocate agents across channels. Thus enabling end-to-end workforce optimization within a single solution and ensuring consistent service levels across all customer interaction channels. This feature is available in all AWS Regions where Amazon Connect Customer agent scheduling is available. To learn more about Amazon Connect Customer agent scheduling, click here.  

Amazon EC2 High Memory U7i instances with 12TB of memory (u7i-12tb.224xlarge) are now available in the AWS Europe (Zurich) region. U7i instances are part of AWS 7th generation and are powered by custom fourth generation Intel Xeon Scalable Processors (Sapphire Rapids). U7i-12tb instances offer 12TiB of DDR5 memory, enabling customers to scale transaction processing throughput in a fast-growing data environment.

U7i-12tb instances offer 896 vCPUs, support up to 100Gbps Elastic Block Storage (EBS) for faster data loading and backups, deliver up to 100Gbps of network bandwidth, and support ENA Express. U7i instances are ideal for customers using mission-critical in-memory databases like SAP HANA, Oracle, and SQL Server.

To learn more about U7i instances, visit the High Memory instances page.

 

​Amazon EC2 High Memory U7i instances with 12TB of memory (u7i-12tb.224xlarge) are now available in the AWS Europe (Zurich) region. U7i instances are part of AWS 7th generation and are powered by custom fourth generation Intel Xeon Scalable Processors (Sapphire Rapids). U7i-12tb instances offer 12TiB of DDR5 memory, enabling customers to scale transaction processing throughput in a fast-growing data environment.
U7i-12tb instances offer 896 vCPUs, support up to 100Gbps Elastic Block Storage (EBS) for faster data loading and backups, deliver up to 100Gbps of network bandwidth, and support ENA Express. U7i instances are ideal for customers using mission-critical in-memory databases like SAP HANA, Oracle, and SQL Server. To learn more about U7i instances, visit the High Memory instances page.